Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits

Lida Wang, Shuang Gao, Siyuan Chen, Havell Markus, Gao Wang, Laura Carrel, Xiang Zhan (), Dajiang J. Liu () and Bibo Jiang ()
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Lida Wang: Pennsylvania State University College of Medicine, Department of Public Health Sciences
Shuang Gao: Pennsylvania State University College of Medicine, Department of Public Health Sciences
Siyuan Chen: Pennsylvania State University College of Medicine, Department of Public Health Sciences
Havell Markus: Pennsylvania State University College of Medicine, Bioinformatics and Genomics PhD Program
Gao Wang: Columbia University, Center for Statistical Genetics, The Gertrude H. Sergievsky Center
Laura Carrel: Pennsylvania State University College of Medicine, Bioinformatics and Genomics PhD Program
Xiang Zhan: Southeast University, School of Statistics and Data Science
Dajiang J. Liu: Pennsylvania State University College of Medicine, Department of Public Health Sciences
Bibo Jiang: Pennsylvania State University College of Medicine, Department of Public Health Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Genome-wide association studies have identified many loci for brain disorders, but most non-coding variants fail to colocalize with bulk expression quantitative trait loci. Single-cell expression quantitative trait loci studies capture cell-type-specific regulation but are often underpowered. We developed Bulk And Single cell expression quantitative trait loci Integration across Cell states (BASIC) to combine bulk and single-cell expression quantitative trait loci through “axis-quantitative trait loci,” which decompose bulk-tissue effects along orthogonal axes of cell-type expression. BASIC better distinguishes shared versus cell-type-specific effects and increases power. Analyzing single-cell expression quantitative trait loci with cortex bulk data from MetaBrain using BASIC identified 5644 additional gene with quantitative trait loci (74.5%), equivalent to a 76.8% increase in sample size. Integrating axis-quantitative trait loci with 12 brain-related traits improved colocalization by 53.5% versus single-cell studies and 111% versus bulk studies, revealing risk genes such as DEDD for Alzheimer’s disease and drug candidates including cabergoline.

Date: 2025
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DOI: 10.1038/s41467-025-65643-w

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